Genomic technology has advanced to a point at which, in principle, it has become possible to determine complete genomic sequences and to quantitatively measure the mRNA levels for each gene expressed in a cell. For some species the complete genomic sequence has now been determined, and for one strain of the yeast Saccharomyces cervisiae, the mRNA levels for each expressed gene have been precisely quantified under different growth conditions (Velculescu et al., 1997). Comparative cDNA array analysis and related technologies have been used to determine induced changes in gene expression at the mRNA level by concurrently monitoring the expression level of a large number of genes (in some cases all the genes) expressed by the investigated cell or tissue (Shalon et al., 1996). Furthermore, biological and computational techniques have been used to correlate specific function with gene sequences. The interpretation of the data obtained by these techniques in the context of the structure, control and mechanism of biological systems has been recognized as a considerable challenge. In particular, it has been extremely difficult to explain the mechanism of biological processes by genomic analysis alone.
Proteins are essential for the control and execution of virtually every biological process. The rate of synthesis and the half-life of proteins and thus their expression level are also controlled post-transcriptionally. Furthermore, the activity of proteins is frequently modulated by post-translational modifications, in particular protein phosphorylation, and dependent on the association of the protein with other molecules including DNA and proteins. Neither the level of expression nor the state of activity of proteins is therefore directly apparent from the gene sequence or even the expression level of the corresponding mRNA transcript. It is therefore essential that a complete description of a biological system include measurements that indicate the identity, quantity and the state of activity of the proteins which constitute the system. The large-scale (ultimately global) analysis of proteins expressed in a cell or tissue has been termed proteome analysis (Pennington et al., 1997).
At present no protein analytical technology approaches the throughput and level of automation of genomic technology. The most common implementation of proteome analysis is based on the separation of complex protein samples most commonly by two-dimensional gel electrophoresis (2DE) and the subsequent sequential identification of the separated protein species (Ducret et al., 1998; Garrels et al., 1997; Link et al., 1997; Shevchenko et al., 1996; Gygi et al. 1999; Boucherie et al., 1996). This approach has been revolutionized by the development of powerful mass spectrometric techniques and the development of computer algorithms which correlate protein and peptide mass spectral data with sequence databases and thus rapidly and conclusively identify proteins (Eng et al., 1994; Mann and Wilm, 1994; Yates et al., 1995). This technology has reached a level of sensitivity which now permits the identification of essentially any protein which is detectable by conventional protein staining methods including silver staining (Figeys and Aebersold, 1998; Figeys et al., 1996; Figeys et al., 1997; Shevchenko et al., 1996). However, the sequential manner in which samples are processed limits the sample throughput, the most sensitive methods have been difficult to automate and low abundance proteins, such as regulatory proteins, escape detection without prior enrichment, thus effectively limiting the dynamic range of the technique. In the 2DE/(MS)n method, proteins are quantified by densitometry of stained spots in the 2DE gels.
The development of methods and instrumentation for automated, data-dependent electrospray ionization (ESI) tandem mass spectrometry (MSn) in conjunction with microcapillary liquid chromatography (μLC) and database searching has significantly increased the sensitivity and speed of the identification of gel-separated proteins. As an alternative to the 2DE/MSn approach to proteome analysis, the direct analysis by tandem mass spectrometry of peptide mixtures generated by the digestion of complex protein mixtures has been proposed (Dongr'e et al., 1997). μLC-MS/MS has also been used successfully for the large-scale identification of individual proteins directly from mixtures without gel electrophoretic separation (Link et al., 1999; Opitek et al., 1997) While these approaches dramatically accelerate protein identification, the quantities of the analyzed proteins cannot be easily determined, and these methods have not been shown to substantially alleviate the dynamic range problem also encountered by the 2DE/MS/MS approach. Therefore, low abundance proteins in complex samples are also difficult to analyze by the μLC/MS/MS method without their prior enrichment.
It is therefore apparent that current technologies, while suitable to identify the components of protein mixtures, are neither capable of measuring the quantity nor the state of activity of the protein in a mixture. Even evolutionary improvements of the current approaches are unlikely to advance their performance sufficiently to make routine quantitative and functional proteome analysis a reality.
This invention provides methods and reagents that can be employed in proteome analysis which overcome the limitations inherent in traditional techniques. The basic approach described can be employed for the quantitative analysis of protein expression in complex samples (such as cells, tissues, and fractions thereof), the detection and quantitation of specific proteins in complex samples, and the quantitative measurement of specific enzymatic activities in complex samples.
In this regard, a multitude of analytical techniques are presently available for clinical and diagnostic assays which detect the presence, absence, deficiency or excess of a protein or protein function associable with a normal or disease state. While these techniques are quite sensitive, they do not necessarily provide chemical speciation of products and may, as a result, be difficult to use for assaying several proteins or enzymes simultaneously in a single sample. Current methods may not distinguish among aberrant expression of different enzymes or their malfunctions which lead to a common set of clinical symptoms. The methods and reagents herein can be employed in clinical and diagnostic assays for simultaneous (multiplex) monitoring of multiple proteins and protein reactions.